import mdp, util
from copy import copy, deepcopy

from learningAgents import ValueEstimationAgent

class ValueIterationAgent(ValueEstimationAgent):
    """
        * Please read learningAgents.py before reading this.*
    
        A ValueIterationAgent takes a Markov decision process
        (see mdp.py) on initialization and runs value iteration
        for a given number of iterations using the supplied
        discount factor.
    """
    def __init__(self, mdp, discount=0.9, iterations=100):
        """
          Your value iteration agent should take an mdp on
          construction, run the indicated number of iterations
          and then act according to the resulting policy.
        
          Some useful mdp methods you will use:
              mdp.getStates()
              mdp.getPossibleActions(state)
              mdp.getTransitionStatesAndProbs(state, action)
              mdp.getReward(state, action, nextState)
        """
        self.mdp = mdp
        self.discount = discount
        self.iterations = iterations
        self.values = util.Counter() # A Counter is a dict with default 0
         
        states = self.mdp.getStates()
        
        "*** YOUR CODE HERE ***"
        for i in range(0, iterations):
            prevValues = copy(self.values)
            
            for state in states:
                if not self.mdp.isTerminal(state):
                    self.values[state] = self.getOptimalValue(state, prevValues)
    
    def getOptimalValue(self, state, prevValues):
        actions = self.mdp.getPossibleActions(state)
        qValues = util.Counter()
        
        for action in actions:
            transitionProbs = self.mdp.getTransitionStatesAndProbs(state, action)
            
            for nextState, prob in transitionProbs:
                qValues[action] += prob * (self.mdp.getReward(state, action, nextState) + self.discount * prevValues[nextState])
       
        # return the largest value
        return qValues[qValues.argMax()]
    
    def getValue(self, state):
        """
          Return the value of the state (computed in __init__).
        """
        return self.values[state]


    def getQValue(self, state, action):
        """
          The q-value of the state action pair
          (after the indicated number of value iteration
          passes).  Note that value iteration does not
          necessarily create this quantity and you may have
          to derive it on the fly.
        """
        "*** YOUR CODE HERE ***"
        transitionProbs = self.mdp.getTransitionStatesAndProbs(state, action)
        qValue = 0
        
        for nextState, prob in transitionProbs:
            qValue += prob * (self.mdp.getReward(state, action, nextState) + self.discount * self.values[nextState])
            
        return qValue

    def getPolicy(self, state):
        """
          The policy is the best action in the given state
          according to the values computed by value iteration.
          You may break ties any way you see fit.
        """
        "*** YOUR CODE HERE ***"
        if self.mdp.isTerminal(state):
            return
        
        qvalues = util.Counter()
        
        for action in self.mdp.getPossibleActions(state):
            qvalues[action] = self.getQValue(state, action)
            
        return qvalues.argMax()

    def getAction(self, state):
        "Returns the policy at the state (no exploration)."
        return self.getPolicy(state)
  
